The Heat in The Trend Point: June 3 to June 7

Living in a globalized world where business operates with an evolving set of practices and norms, there are many areas where enterprise technology is impacted. Several recent articles that point to this idea caught our attention in The Trend Point over the past week.

There is an imminent need for solutions that are geared towards large businesses that are operating on a global scale, and have to deal with large amounts of data. “Getting a Global Perspective on Enterprise Search” echoes this idea:

The first day of the conference started with Ed Dale of Ernst & Young talking about implementing enterprise search for a truly global organisation. E&Y’s search is over a surprisingly small number of documents (only 2 million or so) but they are lucky enough to have a relatively large and experienced team running their search as an ongoing operation – no ‘fire and forget’ here (an approach often taken and seldom successfully).

It is no surprise that we are seeing an article like “Sage Advice for Data Storage and Analytics” call for consideration towards scalability and searchability. The following information was relayed in this post:

The repository should be highly scalable with respect to the storage capacity and amount of requests it can handle. Because of ever generating digital content out of various business processes, size of the stored content can grow rapidly and the storage limit should not be a roadblock for any content repository. Similarly, the architecture should be capable enough of handling a varying number of user requests.

Many terms like semantic search, natural language processing and text analysis are popping up everywhere in regards to enterprise software. We saw the following summary in “SAP HANA Project Addresses Text Analysis” break down some of these definitions:

The two terms are used interchangeably by a lot of people. There is a lot of gray area in defining ‘Text Analysis’ and differentiating it from ‘Text Mining.’ But from the SAP perspective, ‘Text Analysis,’ refers to the ability to do Natural Language Processing, linguistically understand the text and apply statistical techniques to refine the results. Text Mining is applying algorithms, like predictive analytics, for post-processing of data (akin to data mining).

As successful businesses become global they often need increased scalability and text analysis capabilities. One unique feature to Sinequa’s Unified Information Access is that in addition to the semantic search and text analysis functionality (“Natural Language Processing”), this solution also has the capability to interpret text in multiple languages, and it scales to very large volumes. An enterprise search solution is not prepared to enter the globalized market without such technology. Sinequa is particularly poised to address this aspect because their research continues every day. Just as language naturally evolves, Sinequa’s methods also evolve to mirror such changes.

Jane Smith, June 12, 2013

Sponsored by, developer of Beyond Search

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Big Data: Marketing Nirvana or the Next Big Bubble to Burst?

Everyone surfs on the Big Data wave, redefining it such that their roles in this new “hot” market are maximized. Some journalists have already started to blacklist press releases on the subject, since they receive too much fanciful nonsense.

That is a pity for the companies that have really something to offer in this market. If you don’t agree that Big Data really defines a market, let us take a simple approach: We talk about enterprises and administrations that have to deal with vast amounts of data that come from very varied sources and in wildly different formats, and flow into their storage space at great speed. This market is addressed by products and services that help these large organizations not just cope with the deluge of data but extract the useful information contained in it.

Some of the players in the Big Data market must feel reminded of Molière’s Monsieur Jourdain who learnt that he had been “speaking in prose” before knowing what prose was: They had been serving the Big Data market before they knew it would be called that.

At Sinequa, we have been dealing with Big Data (in the above sense) for quite some time: Our Unified Information Access solution has been used by large enterprises and administrations to plough through billions of data base records, business transactions, and unstructured data of all sorts, like documents, emails, and social network data. Our semantic analyses and Natural Language Processing have served to make sense of this magma of data, and to create structure where there was none. All this in order to find sense in chaos. The challenge for us was to combine deep analysis with high performance in dealing with big volumes. We have invested a lot of energy – and dare I say, brain power – in our solution to satisfy big customers like Siemens, Crédit Agricole, Mercer or Atos in their quest to extract useful information from their big data volumes, relevant for their employees and customers.

The Grail of the Structured Universe

For many years, IT professionals have been chasing the grail of the “all-structured” enterprise data. This is how engineers were educated: you must structure the world to get a grip on it. If you need to search, you haven’t done your homework. For many of them, it is thus painful to give up on this goal – and on years of work and huge investments – in order to turn to search technology that can cope with the unstructured world much more easily and demanding an order of magnitude less time and money. Thus, search technology has evolved and is now used at the core of Unified Information Access platforms.

It’s not all or nothing

Now let’s not fall into the trap of claiming that Big Data is all about search and our kind of content analytics, just because we have been in Big Data up to our ears long before the people who invented the name. There are many approaches to Big Data and many useful tools and solutions to deal with it. But Unified Information Access platforms and semantic technologies are certainly part of any complete solution set. And our customers benefit from the fact that we have been in Big Data quite some time before the concept entered the hype cycle: Our solutions have matured over time.

Is Big Data a bubble that will burst?

If you link it inseparably to its name, “Big Data”, then it might well disappear. But the very real problems of Big Data sketched above will not go away. Heterogeneous and continuously changing big data volumes will increase rather than diminish.

See also

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